{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('sequence', (19, 1)), ('seq_enc', (10, 1)), ('seq_dec', (9, 1)), ('input', (1, 10, 1, 64, 64)), ('match', (1, 10, 1, 64, 64)), ('input_p', (10, 1, 1, 64, 64)), ('match_p', (10, 1, 1, 64, 64)), ('dummy', (1, 1, 128, 64, 64)), ('dummy_dummy_0_split_0', (1, 1, 128, 64, 64)), ('dummy_dummy_0_split_1', (1, 1, 128, 64, 64)), ('encode1', (10, 1, 128, 64, 64)), ('encode1_h', (1, 1, 128, 64, 64)), ('encode1_c', (1, 1, 128, 64, 64)), ('input_decode1', (9, 1, 1, 64, 64)), ('decode1', (9, 1, 128, 64, 64)), ('decode1_h', (1, 1, 128, 64, 64)), ('decode1_c', (1, 1, 128, 64, 64)), ('encode1_top_discard', (9, 1, 128, 64, 64)), ('encode1_slice', (1, 1, 128, 64, 64)), ('decode', (10, 1, 128, 64, 64)), ('output', (10, 1, 1, 64, 64)), ('out_flat', (10, 4096)), ('out_flat_flat_data_0_split_0', (10, 4096)), ('out_flat_flat_data_0_split_1', (10, 4096)), ('match_flat', (10, 4096)), ('match_flat_flat_match_0_split_0', (10, 4096)), ('match_flat_flat_match_0_split_1', (10, 4096)), ('cross_entropy_loss', ()), ('out_sigm', (10, 4096)), ('l2_error', ())]\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.insert(0,'/home/csunix/schtmt/NewFolder/caffe_Sep/python')\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import caffe\n",
    "import h5py\n",
    "\n",
    "#% matplotlib inline#THIS LINE HAS ERROR -> USE THE FOLLOWING 2 LINES\n",
    "# from IPython import get_ipython\n",
    "# get_ipython().run_line_magic('matplotlib', 'inline')\n",
    "\n",
    "caffe.set_device(0)\n",
    "caffe.set_mode_gpu()\n",
    "# \n",
    "# load the test model\n",
    "net = caffe.Net('encode-decode_Sep_test.prototxt',\n",
    "                '/usr/not-backed-up/1_convlstm/mnist_convlstm_AE/iter_iter_261000.caffemodel', \n",
    "                caffe.TEST)\n",
    "# print the input and output tensor infos,\n",
    "# for layer_name, blob in net.blobs.items():\n",
    "#     print(layer_name + str(blob.data.shape))\n",
    "print([(k, v.data.shape) for k, v in net.blobs.items()]) # Python data type: list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('encode1', (512, 1, 5, 5), (1,)), ('decode1', (1,), (1,)), ('output_conv', (1, 128, 1, 1), (1,))]\n",
      "(1000, 10, 1, 64, 64)\n",
      "(1000, 10, 1, 64, 64)\n",
      "(1, 10, 1, 64, 64)\n"
     ]
    }
   ],
   "source": [
    "# \n",
    "# # weight infos,\n",
    "print([(k, v[0].data.shape, v[1].data.shape) for k, v in net.params.items()])\n",
    "# for k,v in net.params.items():\n",
    "#     print(k + str(v[0].data.shape) + str(v[1].data.shape))\n",
    "    \n",
    "h5f = h5py.File('/usr/not-backed-up/1_convlstm/bouncing_mnist_autoencoder_test.h5','r')\n",
    "data = h5f['input'][:] \n",
    "match = h5f['match'][:]\n",
    "print(data.shape)\n",
    "print(match.shape)\n",
    "data_input = np.reshape(data[100,:,:,:,:],[1,10,1,64,64])\n",
    "match_input = np.reshape(match[100,:,:,:,:],[1,10,1,64,64])\n",
    "print(data_input.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "net.blobs['input'].reshape(1,10,1,64,64)\n",
    "net.blobs['input'].data[...] = data_input\n",
    "net.blobs['match'].reshape(1,10,1,64,64)\n",
    "net.blobs['match'].data[...] = match_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "out = net.forward()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<built-in method keys of dict object at 0x7f6d91031e88>\n"
     ]
    }
   ],
   "source": [
    "print(out.keys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 10, 1, 64, 64)\n",
      "(1, 10, 1, 64, 64)\n",
      "(10, 1, 1, 64, 64)\n"
     ]
    }
   ],
   "source": [
    "in_ = net.blobs['input'].data\n",
    "print(in_.shape)\n",
    "match_ = net.blobs['match'].data\n",
    "print(match_.shape)\n",
    "recons = net.blobs['output'].data\n",
    "print(recons.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d46ae1fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(64,64))\n",
    "# plt.suptitle('input', fontsize=160)\n",
    "for i in range(10):\n",
    "#     print(i)\n",
    "    plt.subplot(1,10,i+1)\n",
    "    im = np.reshape(in_[:,i,:,:,:],[64,64])\n",
    "#     print(im.shape)\n",
    "    plt.imshow(im)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d3f886910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# match\n",
    "plt.figure(figsize=(64,64))\n",
    "for i in range(10):\n",
    "#     print(i)\n",
    "    plt.subplot(1,10,i+1)\n",
    "    im = np.reshape(match_[:,i,:,:,:],[64,64])\n",
    "#     print(im.shape)\n",
    "    plt.imshow(im)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d3f8869d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# reconstruction\n",
    "plt.figure(figsize=(64,64))\n",
    "for i in range(10):\n",
    "#     print(i)\n",
    "    plt.subplot(1,10,i+1)\n",
    "    im = np.reshape(recons[i,:,:,:,:],[64,64])\n",
    "#     print(im.shape)\n",
    "    plt.imshow(im)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "88.0792999268\n"
     ]
    }
   ],
   "source": [
    "loss = net.blobs['cross_entropy_loss'].data\n",
    "print(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.312908500433\n"
     ]
    }
   ],
   "source": [
    "l2 = net.blobs['l2_error'].data\n",
    "print(l2)"
   ]
  }
 ],
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